Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems

Abstract

In health monitoring systems, the base station (BS) and the wearable sensors communicate with each other to construct a virtual multiple input and multiple output (VMIMO) system. In real applications, the signal that the BS received is a distributed source because of the scattering, reflection, diffraction and refraction in the propagation path. In this paper, a 2D direction-of-arrival (DOA) estimation algorithm for incoherently-distributed (ID) and coherently-distributed (CD) sources is proposed based on multiple VMIMO systems. ID and CD sources are separated through the second-order blind identification (SOBI) algorithm. The traditional estimating signal parameters via the rotational invariance technique (ESPRIT)-based algorithm is valid only for one-dimensional (1D) DOA estimation for the ID source. By constructing the signal subspace, two rotational invariant relationships are constructed. Then, we extend the ESPRIT to estimate 2D DOAs for ID sources. For DOA estimation of CD sources, two rational invariance relationships are constructed based on the application of generalized steering vectors (GSVs). Then, the ESPRIT-based algorithm is used for estimating the eigenvalues of two rational invariance matrices, which contain the angular parameters. The expressions of azimuth and elevation for ID and CD sources have closed forms, which means that the spectrum peak searching is avoided. Therefore, compared to the traditional 2D DOA estimation algorithms, the proposed algorithm imposes significantly low computational complexity. The intersecting point of two rays, which come from two different directions measured by two uniform rectangle arrays (URA), can be regarded as the location of the biosensor (wearable sensor). Three BSs adopting the smart antenna (SA) technique cooperate with each other to locate the wearable sensors using the angulation positioning method. Simulation results demonstrate the effectiveness of the proposed algorithm.

Design and Modelling of Graphene-Based Flexible 5G Antenna for Next-Generation Wearable Head Imaging Systems

“…The above discussion provides a deep insight into the potential applications and advantages of 5G in healthcare. The usage of 5G in wireless communication, remote patient diagnosis, and continuous remote monitoring gained a growing interest in telemedicine [ 22 , 23 , 24 ]. The basic concept of telemedicine is that the patient’s biological parameters are monitored using multiple sensors.…”

Weighted Block Sparse Recovery Algorithm for High Resolution DOA Estimation with Unknown Mutual Coupling

“…The above traditional DOA estimation algorithms are generally known as subspace-based algorithms. Because they are mainly based on eigenvalue decomposition (EVD) or singular value decomposition (SVD) of covariance matrix for DOA estimation, the performance is reduced in the case of low signal to noise ratio (SNR) or a limited number of snapshots [10][11][12][13]. In order to deal with the problems associated with traditional DOA estimation methods, the SSR algorithms, such as l 1 -SVD algorithm [14], sparse Bayesian learning (SBL) algorithm [15,16], l 1 -sparse representation of array covariance vector (SRACV) algorithm [17], and their derivative algorithms [18] were proposed in the past few years.…”